Seagrass_biomass_regression_2017
Lia Domke
10/21/2019
We’re curious to see if there is a relationship between longest leaf length to total shoot biomass. Data available: In 2017 21 sites were surveyed for seagrass. At each site, 8-0.25m^2 quadrats were surveyed and 5 entire shoots (i.e. multiple leaves) were removed and brought back to the lab to be measured (in cm). Each shoot (n = 840) was weighed to get total fresh and dry biomass (in grams) and each leaf on the shoot had its length (cm) and width (cm) measured.
We are interested in the length of the longest leaf and the dry biomass.
If there is a strong relationship, then I would like to use the linear model from 2017 to predict biomass values based on longest leaf length in 2019. # Data import
eelgrass <- read.csv("../APECS Master repository/APECS Master repo/ALL_DATA/seagrass_biometrics_CLEAN.csv")Libraries
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.4.0 ✔ purrr 0.3.4
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## ✔ readr 2.1.1 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectData Cleaning
# extract what we're interested in
str(eelgrass)## 'data.frame': 840 obs. of 71 variables:
## $ site : chr "2017_H_01" "2017_H_01" "2017_H_01" "2017_H_01" ...
## $ collection_date : chr "29/4/17" "29/4/17" "29/4/17" "29/4/17" ...
## $ YYYYMMDD : int 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num 0.497 NA NA NA NA ...
## $ caprel_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : chr "" "" "" "" ...
## $ gastropod_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : chr "" "" "" "" ...
## $ notes : chr "" "" "" "" ...eel <- eelgrass %>%
mutate(shoot_dw = shoot_foil_dw-shoot_foil) # calculate dry weight
# determin max leaf length
eel2 <- eel %>%
rowwise() %>%
mutate(max_length = max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
leaf_length5, leaf_length6, leaf_length7, leaf_length8,
leaf_length9, leaf_length10, na.rm=TRUE)) %>%
mutate(mean_length = mean(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
leaf_length5, leaf_length6, leaf_length7, leaf_length8,
leaf_length9, leaf_length10, na.rm=TRUE))## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Infstr(eel2)## rowws_df [840 × 74] (S3: rowwise_df/tbl_df/tbl/data.frame)
## $ site : chr [1:840] "2017_H_01" "2017_H_01" "2017_H_01" "2017_H_01" ...
## $ collection_date : chr [1:840] "29/4/17" "29/4/17" "29/4/17" "29/4/17" ...
## $ YYYYMMDD : int [1:840] 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int [1:840] 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int [1:840] 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num [1:840] 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num [1:840] 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num [1:840] 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num [1:840] 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num [1:840] 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num [1:840] 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num [1:840] 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num [1:840] 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num [1:840] 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num [1:840] 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num [1:840] 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num [1:840] NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num [1:840] NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num [1:840] 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num [1:840] 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num [1:840] 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num [1:840] 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num [1:840] 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num [1:840] NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num [1:840] NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num [1:840] 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num [1:840] 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num [1:840] 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num [1:840] 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num [1:840] 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num [1:840] 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num [1:840] 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num [1:840] 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num [1:840] 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num [1:840] 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num [1:840] 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int [1:840] 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num [1:840] 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num [1:840] 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num [1:840] 0.497 NA NA NA NA ...
## $ caprel_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : chr [1:840] "" "" "" "" ...
## $ gastropod_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int [1:840] 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : chr [1:840] "" "" "" "" ...
## $ notes : chr [1:840] "" "" "" "" ...
## $ shoot_dw : num [1:840] 0.039 0.0629 0.0247 0.0489 0.0167 ...
## $ max_length : num [1:840] 22.1 25.2 16.4 21 13.6 32.4 23 22.7 26 26.5 ...
## $ mean_length : num [1:840] 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## - attr(*, "groups")= tibble [840 × 1] (S3: tbl_df/tbl/data.frame)
## ..$ .rows: list<int> [1:840]
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## .. .. [list output truncated]
## .. ..@ ptype: int(0)# subset only the data that was collected before July (the main part of the growing season)
eel_beforeJuly <- eel2 %>%
filter(YYYYMMDD < 20170701) # before July
eel_sub <- eel2 # all data
str(eel_sub)## rowws_df [840 × 74] (S3: rowwise_df/tbl_df/tbl/data.frame)
## $ site : chr [1:840] "2017_H_01" "2017_H_01" "2017_H_01" "2017_H_01" ...
## $ collection_date : chr [1:840] "29/4/17" "29/4/17" "29/4/17" "29/4/17" ...
## $ YYYYMMDD : int [1:840] 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int [1:840] 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int [1:840] 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num [1:840] 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num [1:840] 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num [1:840] 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num [1:840] 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num [1:840] 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num [1:840] 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num [1:840] 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num [1:840] 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num [1:840] 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num [1:840] 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num [1:840] 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num [1:840] NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num [1:840] NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num [1:840] 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num [1:840] 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num [1:840] 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num [1:840] 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num [1:840] 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num [1:840] NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num [1:840] NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num [1:840] 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num [1:840] 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num [1:840] 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num [1:840] 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num [1:840] 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num [1:840] 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num [1:840] 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num [1:840] 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num [1:840] 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num [1:840] 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num [1:840] 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int [1:840] 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num [1:840] 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num [1:840] 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num [1:840] 0.497 NA NA NA NA ...
## $ caprel_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : chr [1:840] "" "" "" "" ...
## $ gastropod_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num [1:840] NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int [1:840] 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : chr [1:840] "" "" "" "" ...
## $ notes : chr [1:840] "" "" "" "" ...
## $ shoot_dw : num [1:840] 0.039 0.0629 0.0247 0.0489 0.0167 ...
## $ max_length : num [1:840] 22.1 25.2 16.4 21 13.6 32.4 23 22.7 26 26.5 ...
## $ mean_length : num [1:840] 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## - attr(*, "groups")= tibble [840 × 1] (S3: tbl_df/tbl/data.frame)
## ..$ .rows: list<int> [1:840]
## .. ..$ : int 1
## .. ..$ : int 2
## .. ..$ : int 3
## .. ..$ : int 4
## .. ..$ : int 5
## .. ..$ : int 6
## .. ..$ : int 7
## .. ..$ : int 8
## .. ..$ : int 9
## .. ..$ : int 10
## .. ..$ : int 11
## .. ..$ : int 12
## .. ..$ : int 13
## .. ..$ : int 14
## .. ..$ : int 15
## .. ..$ : int 16
## .. ..$ : int 17
## .. ..$ : int 18
## .. ..$ : int 19
## .. ..$ : int 20
## .. ..$ : int 21
## .. ..$ : int 22
## .. ..$ : int 23
## .. ..$ : int 24
## .. ..$ : int 25
## .. ..$ : int 26
## .. ..$ : int 27
## .. ..$ : int 28
## .. ..$ : int 29
## .. ..$ : int 30
## .. ..$ : int 31
## .. ..$ : int 32
## .. ..$ : int 33
## .. ..$ : int 34
## .. ..$ : int 35
## .. ..$ : int 36
## .. ..$ : int 37
## .. ..$ : int 38
## .. ..$ : int 39
## .. ..$ : int 40
## .. ..$ : int 41
## .. ..$ : int 42
## .. ..$ : int 43
## .. ..$ : int 44
## .. ..$ : int 45
## .. ..$ : int 46
## .. ..$ : int 47
## .. ..$ : int 48
## .. ..$ : int 49
## .. ..$ : int 50
## .. ..$ : int 51
## .. ..$ : int 52
## .. ..$ : int 53
## .. ..$ : int 54
## .. ..$ : int 55
## .. ..$ : int 56
## .. ..$ : int 57
## .. ..$ : int 58
## .. ..$ : int 59
## .. ..$ : int 60
## .. ..$ : int 61
## .. ..$ : int 62
## .. ..$ : int 63
## .. ..$ : int 64
## .. ..$ : int 65
## .. ..$ : int 66
## .. ..$ : int 67
## .. ..$ : int 68
## .. ..$ : int 69
## .. ..$ : int 70
## .. ..$ : int 71
## .. ..$ : int 72
## .. ..$ : int 73
## .. ..$ : int 74
## .. ..$ : int 75
## .. ..$ : int 76
## .. ..$ : int 77
## .. ..$ : int 78
## .. ..$ : int 79
## .. ..$ : int 80
## .. ..$ : int 81
## .. ..$ : int 82
## .. ..$ : int 83
## .. ..$ : int 84
## .. ..$ : int 85
## .. ..$ : int 86
## .. ..$ : int 87
## .. ..$ : int 88
## .. ..$ : int 89
## .. ..$ : int 90
## .. ..$ : int 91
## .. ..$ : int 92
## .. ..$ : int 93
## .. ..$ : int 94
## .. ..$ : int 95
## .. ..$ : int 96
## .. ..$ : int 97
## .. ..$ : int 98
## .. ..$ : int 99
## .. .. [list output truncated]
## .. ..@ ptype: int(0)levels(as.factor(eel_sub$YYYYMMDD))## [1] "20170429" "20170524" "20170525" "20170528" "20170622" "20170623"
## [7] "20170624" "20170625" "20170627" "20170628" "20170722" "20170723"
## [13] "20170724" "20170725" "20170726" "20170820" "20170821" "20170822"summary(eel_sub)## site collection_date YYYYMMDD quadrat
## Length:840 Length:840 Min. :20170429 Min. :1.00
## Class :character Class :character 1st Qu.:20170525 1st Qu.:2.75
## Mode :character Mode :character Median :20170625 Median :4.50
## Mean :20170630 Mean :4.50
## 3rd Qu.:20170724 3rd Qu.:6.25
## Max. :20170822 Max. :8.00
##
## plant rhi_length node1 node2 node3
## Min. :1 Min. :0.800 Min. :0.100 Min. :0.100 Min. :0.1000
## 1st Qu.:2 1st Qu.:5.000 1st Qu.:0.600 1st Qu.:0.700 1st Qu.:0.5000
## Median :3 Median :5.000 Median :1.000 Median :1.100 Median :0.9000
## Mean :3 Mean :4.608 Mean :1.079 Mean :1.246 Mean :0.9314
## 3rd Qu.:4 3rd Qu.:5.000 3rd Qu.:1.400 3rd Qu.:1.700 3rd Qu.:1.3000
## Max. :5 Max. :5.000 Max. :3.100 Max. :3.600 Max. :2.7000
## NA's :7 NA's :7 NA's :25 NA's :183
## node4 node5 leaf_length1 leaf_length2
## Min. :0.100 Min. :0.1000 Min. : 2.30 Min. : 1.70
## 1st Qu.:0.400 1st Qu.:0.3000 1st Qu.: 22.70 1st Qu.: 22.20
## Median :0.600 Median :0.4000 Median : 35.00 Median : 36.70
## Mean :0.684 Mean :0.4946 Mean : 39.04 Mean : 40.05
## 3rd Qu.:0.900 3rd Qu.:0.7000 3rd Qu.: 50.80 3rd Qu.: 53.20
## Max. :8.000 Max. :1.5000 Max. :107.00 Max. :133.20
## NA's :359 NA's :508 NA's :7 NA's :7
## leaf_length3 leaf_length4 leaf_length5 leaf_length6
## Min. : 3.10 Min. : 2.10 Min. : 1.90 Min. : 2.10
## 1st Qu.: 19.60 1st Qu.: 18.60 1st Qu.: 17.05 1st Qu.: 13.80
## Median : 32.80 Median : 33.20 Median : 31.20 Median : 29.00
## Mean : 37.33 Mean : 36.65 Mean : 35.01 Mean : 33.01
## 3rd Qu.: 50.20 3rd Qu.: 49.30 3rd Qu.: 46.30 3rd Qu.: 45.65
## Max. :125.80 Max. :126.00 Max. :124.20 Max. :113.00
## NA's :8 NA's :31 NA's :249 NA's :585
## leaf_length7 leaf_length8 leaf_length9 leaf_length10
## Min. : 1.30 Min. : 2.600 Min. : 3.00 Min. :16.00
## 1st Qu.:13.00 1st Qu.: 7.825 1st Qu.: 8.25 1st Qu.:18.18
## Median :27.50 Median :21.850 Median :14.05 Median :20.35
## Mean :29.43 Mean :29.128 Mean :19.22 Mean :20.35
## 3rd Qu.:42.65 3rd Qu.:38.450 3rd Qu.:28.02 3rd Qu.:22.52
## Max. :82.60 Max. :97.200 Max. :45.20 Max. :24.70
## NA's :744 NA's :808 NA's :834 NA's :838
## leaf_width1 leaf_width2 leaf_width3 leaf_width4
## Min. :0.100 Min. :0.1000 Min. :0.10 Min. :0.1000
## 1st Qu.:0.300 1st Qu.:0.3000 1st Qu.:0.30 1st Qu.:0.4000
## Median :0.400 Median :0.4000 Median :0.40 Median :0.4000
## Mean :0.408 Mean :0.4139 Mean :0.42 Mean :0.4223
## 3rd Qu.:0.500 3rd Qu.:0.5000 3rd Qu.:0.50 3rd Qu.:0.5000
## Max. :0.800 Max. :0.8000 Max. :0.80 Max. :0.8000
## NA's :7 NA's :7 NA's :8 NA's :31
## leaf_width5 leaf_width6 leaf_width7 leaf_width8
## Min. :0.1000 Min. :0.1000 Min. :0.2000 Min. :0.2
## 1st Qu.:0.3000 1st Qu.:0.3000 1st Qu.:0.3000 1st Qu.:0.3
## Median :0.4000 Median :0.4000 Median :0.4000 Median :0.4
## Mean :0.4206 Mean :0.4051 Mean :0.3979 Mean :0.4
## 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5
## Max. :0.8000 Max. :0.7000 Max. :0.7000 Max. :0.6
## NA's :249 NA's :585 NA's :744 NA's :808
## leaf_width9 leaf_width10 pad_mass_g pad_epiphyte_mass_g
## Min. :0.2000 Min. :0.300 Min. :0.3216 Min. :0.3247
## 1st Qu.:0.3000 1st Qu.:0.325 1st Qu.:0.6301 1st Qu.:0.6357
## Median :0.3000 Median :0.350 Median :0.6620 Median :0.6694
## Mean :0.3167 Mean :0.350 Mean :0.6585 Mean :0.6658
## 3rd Qu.:0.3750 3rd Qu.:0.375 3rd Qu.:0.6954 3rd Qu.:0.7025
## Max. :0.4000 Max. :0.400 Max. :0.8519 Max. :0.9698
## NA's :834 NA's :838 NA's :17 NA's :17
## rhi_mass_fw rhi_foil rhi_foil_dw_g shoot_mass_fw
## Min. :0.0353 Min. :0.2876 Min. :0.3297 Min. :0.0293
## 1st Qu.:0.4226 1st Qu.:0.5985 1st Qu.:0.6867 1st Qu.:0.7381
## Median :0.5814 Median :0.7381 Median :0.8214 Median :1.3285
## Mean :0.6008 Mean :0.7875 Mean :0.8706 Mean :1.6456
## 3rd Qu.:0.7535 3rd Qu.:0.9577 3rd Qu.:1.0481 3rd Qu.:2.1778
## Max. :1.9121 Max. :1.4849 Max. :1.6390 Max. :6.6599
## NA's :7 NA's :7 NA's :7 NA's :7
## shoot_foil shoot_foil_dw xs_shoot_mass_fw xs_shoot_foil
## Min. :0.4336 Min. :0.4864 Min. : 0.0243 Min. :0.5372
## 1st Qu.:1.2024 1st Qu.:1.3980 1st Qu.: 3.2862 1st Qu.:1.3737
## Median :1.6170 Median :1.9205 Median : 6.9612 Median :2.0462
## Mean :1.6078 Mean :1.8629 Mean :10.1766 Mean :2.1232
## 3rd Qu.:2.0314 3rd Qu.:2.3504 3rd Qu.:12.7799 3rd Qu.:2.6255
## Max. :3.1062 Max. :3.3807 Max. :66.8455 Max. :5.3852
## NA's :7 NA's :7 NA's :672 NA's :672
## xs_shoot_foil_dw xs_pad_mass_g xs_epiphyte_pad_mass_g iso_count
## Min. : 0.6509 Min. :0.6426 Min. :0.6730 Min. : 1.000
## 1st Qu.: 2.2486 1st Qu.:0.6596 1st Qu.:0.6908 1st Qu.: 1.000
## Median : 2.9657 Median :0.6766 Median :0.7087 Median : 1.000
## Mean : 3.3922 Mean :0.6766 Mean :0.7087 Mean : 2.405
## 3rd Qu.: 3.9205 3rd Qu.:0.6937 3rd Qu.:0.7266 3rd Qu.: 2.000
## Max. :12.5251 Max. :0.7107 Max. :0.7444 Max. :20.000
## NA's :672 NA's :838 NA's :838 NA's :761
## iso_fw iso_foil iso_foil_dw gamm_amph_count
## Min. :0.0002 Min. :0.2344 Min. :0.2346 Min. : 1.000
## 1st Qu.:0.0031 1st Qu.:0.4008 1st Qu.:0.4327 1st Qu.: 1.000
## Median :0.0181 Median :0.4801 Median :0.5111 Median : 3.000
## Mean :0.1226 Mean :0.4996 Mean :0.5290 Mean : 4.649
## 3rd Qu.:0.1878 3rd Qu.:0.5650 3rd Qu.:0.5772 3rd Qu.: 6.000
## Max. :1.2169 Max. :0.9067 Max. :0.9509 Max. :33.000
## NA's :761 NA's :761 NA's :761 NA's :746
## gamm_amph_fw gamm_amph_foil gamm_amph_foil_dw caprel_count
## Min. :0.0004 Min. :0.2516 Min. :0.2516 Min. : 1.000
## 1st Qu.:0.0031 1st Qu.:0.3984 1st Qu.:0.3988 1st Qu.: 1.000
## Median :0.0067 Median :0.4884 Median :0.4894 Median : 3.000
## Mean :0.0118 Mean :0.4941 Mean :0.4950 Mean : 5.695
## 3rd Qu.:0.0123 3rd Qu.:0.5392 3rd Qu.:0.5395 3rd Qu.: 7.750
## Max. :0.1460 Max. :1.0428 Max. :1.0438 Max. :28.000
## NA's :747 NA's :747 NA's :747 NA's :758
## caprel_fw caprel_foil caprel_foil_dw limpet_count
## Min. :0.0003 Min. :0.0001 Min. :0.0002 Min. : 1.000
## 1st Qu.:0.0026 1st Qu.:0.3694 1st Qu.:0.3701 1st Qu.: 1.000
## Median :0.0082 Median :0.4709 Median :0.4713 Median : 3.000
## Mean :0.0320 Mean :0.4678 Mean :0.4693 Mean : 4.216
## 3rd Qu.:0.0275 3rd Qu.:0.5586 3rd Qu.:0.5591 3rd Qu.: 5.000
## Max. :0.6490 Max. :1.0819 Max. :1.0860 Max. :17.000
## NA's :763 NA's :759 NA's :759 NA's :738
## limpet_fw limpet_foil limpet_foil_dw gastropod_count
## Min. :0.0037 Min. :0.2127 Min. :0.2152 Min. : 1.000
## 1st Qu.:0.0280 1st Qu.:0.4113 1st Qu.:0.4436 1st Qu.: 2.000
## Median :0.0634 Median :0.4988 Median :0.5363 Median : 3.000
## Mean :0.1240 Mean :0.5046 Mean :0.5434 Mean : 8.767
## 3rd Qu.:0.1509 3rd Qu.:0.5760 3rd Qu.:0.6236 3rd Qu.: 9.750
## Max. :0.8388 Max. :1.0519 Max. :1.0611 Max. :70.000
## NA's :738 NA's :738 NA's :738 NA's :754
## gastropod_fw gastropod_foil gastropod_foil_dw crab_count
## Length:840 Min. :0.0001 Min. :0.0002 Min. :1
## Class :character 1st Qu.:0.3753 1st Qu.:0.4020 1st Qu.:1
## Mode :character Median :0.4532 Median :0.4832 Median :1
## Mean :0.4802 Mean :0.5010 Mean :1
## 3rd Qu.:0.5550 3rd Qu.:0.5772 3rd Qu.:1
## Max. :1.0419 Max. :1.0514 Max. :1
## NA's :753 NA's :753 NA's :839
## crab_fw crab_foil crab_foil_dw polychaete_count
## Min. :0.1673 Min. :0.3328 Min. :0.3593 Min. : 0.000
## 1st Qu.:0.1673 1st Qu.:0.3328 1st Qu.:0.3593 1st Qu.: 2.250
## Median :0.1673 Median :0.3328 Median :0.3593 Median : 4.000
## Mean :0.1673 Mean :0.3328 Mean :0.3593 Mean : 5.267
## 3rd Qu.:0.1673 3rd Qu.:0.3328 3rd Qu.:0.3593 3rd Qu.: 6.000
## Max. :0.1673 Max. :0.3328 Max. :0.3593 Max. :35.000
## NA's :839 NA's :839 NA's :839 NA's :690
## other_animal_notes notes shoot_dw max_length
## Length:840 Length:840 Min. :0.0042 Min. : -Inf
## Class :character Class :character 1st Qu.:0.1261 1st Qu.: 35.67
## Mode :character Mode :character Median :0.2074 Median : 49.20
## Mean :0.2551 Mean : -Inf
## 3rd Qu.:0.3250 3rd Qu.: 66.83
## Max. :1.3277 Max. :133.20
## NA's :7
## mean_length
## Min. : 2.30
## 1st Qu.: 22.70
## Median : 35.00
## Mean : 39.04
## 3rd Qu.: 50.80
## Max. :107.00
## NA's :7eel_sub2 <- na.omit(data.frame(eel_sub$shoot_dw,
eel_sub$max_length,
eel_sub$mean_length,
eel_sub$shoot_mass_fw))
names(eel_sub2) <- c("shoot_dw", "max_length", "mean_length", "shoot_mass_fw")
summary(eel_beforeJuly)## site collection_date YYYYMMDD quadrat
## Length:520 Length:520 Min. :20170429 Min. :1.00
## Class :character Class :character 1st Qu.:20170524 1st Qu.:2.75
## Mode :character Mode :character Median :20170528 Median :4.50
## Mean :20170549 Mean :4.50
## 3rd Qu.:20170624 3rd Qu.:6.25
## Max. :20170628 Max. :8.00
##
## plant rhi_length node1 node2 node3
## Min. :1 Min. :0.800 Min. :0.100 Min. :0.100 Min. :0.1000
## 1st Qu.:2 1st Qu.:4.300 1st Qu.:0.600 1st Qu.:0.600 1st Qu.:0.4000
## Median :3 Median :5.000 Median :0.900 Median :1.100 Median :0.7000
## Mean :3 Mean :4.439 Mean :1.018 Mean :1.196 Mean :0.8411
## 3rd Qu.:4 3rd Qu.:5.000 3rd Qu.:1.300 3rd Qu.:1.600 3rd Qu.:1.2000
## Max. :5 Max. :5.000 Max. :3.000 Max. :3.600 Max. :2.7000
## NA's :7 NA's :7 NA's :19 NA's :116
## node4 node5 leaf_length1 leaf_length2
## Min. :0.1000 Min. :0.1000 Min. : 2.30 Min. : 1.70
## 1st Qu.:0.3000 1st Qu.:0.2000 1st Qu.: 19.00 1st Qu.: 18.30
## Median :0.5000 Median :0.3000 Median : 28.40 Median : 30.30
## Mean :0.5618 Mean :0.3634 Mean : 33.15 Mean : 33.83
## 3rd Qu.:0.7000 3rd Qu.:0.5000 3rd Qu.: 42.40 3rd Qu.: 46.20
## Max. :8.0000 Max. :1.5000 Max. :105.20 Max. :110.50
## NA's :206 NA's :293 NA's :7 NA's :7
## leaf_length3 leaf_length4 leaf_length5 leaf_length6
## Min. : 3.10 Min. : 2.10 Min. : 1.90 Min. : 2.10
## 1st Qu.: 14.45 1st Qu.:15.55 1st Qu.: 15.65 1st Qu.:12.53
## Median : 26.00 Median :28.00 Median : 26.40 Median :27.10
## Mean : 31.11 Mean :31.02 Mean : 29.54 Mean :30.06
## 3rd Qu.: 43.10 3rd Qu.:41.90 3rd Qu.: 39.25 3rd Qu.:40.35
## Max. :109.90 Max. :96.00 Max. :111.70 Max. :94.20
## NA's :8 NA's :22 NA's :117 NA's :310
## leaf_length7 leaf_length8 leaf_length9 leaf_length10
## Min. : 1.30 Min. : 2.600 Min. : 3.00 Min. :16.00
## 1st Qu.:14.45 1st Qu.: 7.475 1st Qu.: 8.25 1st Qu.:18.18
## Median :25.00 Median :21.000 Median :14.05 Median :20.35
## Mean :28.79 Mean :27.190 Mean :19.22 Mean :20.35
## 3rd Qu.:41.15 3rd Qu.:36.900 3rd Qu.:28.02 3rd Qu.:22.52
## Max. :82.60 Max. :97.200 Max. :45.20 Max. :24.70
## NA's :433 NA's :490 NA's :514 NA's :518
## leaf_width1 leaf_width2 leaf_width3 leaf_width4
## Min. :0.1000 Min. :0.100 Min. :0.1000 Min. :0.1000
## 1st Qu.:0.3000 1st Qu.:0.300 1st Qu.:0.3000 1st Qu.:0.3000
## Median :0.4000 Median :0.400 Median :0.4000 Median :0.4000
## Mean :0.3739 Mean :0.383 Mean :0.3918 Mean :0.3946
## 3rd Qu.:0.5000 3rd Qu.:0.500 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :0.8000 Max. :0.800 Max. :0.8000 Max. :0.8000
## NA's :7 NA's :7 NA's :8 NA's :22
## leaf_width5 leaf_width6 leaf_width7 leaf_width8
## Min. :0.100 Min. :0.1000 Min. :0.2000 Min. :0.2000
## 1st Qu.:0.300 1st Qu.:0.3000 1st Qu.:0.3000 1st Qu.:0.3000
## Median :0.400 Median :0.4000 Median :0.4000 Median :0.4000
## Mean :0.394 Mean :0.3857 Mean :0.3851 Mean :0.3867
## 3rd Qu.:0.500 3rd Qu.:0.5000 3rd Qu.:0.4000 3rd Qu.:0.4000
## Max. :0.800 Max. :0.7000 Max. :0.7000 Max. :0.6000
## NA's :117 NA's :310 NA's :433 NA's :490
## leaf_width9 leaf_width10 pad_mass_g pad_epiphyte_mass_g
## Min. :0.2000 Min. :0.300 Min. :0.4728 Min. :0.4806
## 1st Qu.:0.3000 1st Qu.:0.325 1st Qu.:0.6298 1st Qu.:0.6351
## Median :0.3000 Median :0.350 Median :0.6655 Median :0.6735
## Mean :0.3167 Mean :0.350 Mean :0.6603 Mean :0.6667
## 3rd Qu.:0.3750 3rd Qu.:0.375 3rd Qu.:0.7017 3rd Qu.:0.7066
## Max. :0.4000 Max. :0.400 Max. :0.8519 Max. :0.9698
## NA's :514 NA's :518 NA's :17 NA's :17
## rhi_mass_fw rhi_foil rhi_foil_dw_g shoot_mass_fw
## Min. :0.0353 Min. :0.2876 Min. :0.3297 Min. :0.0293
## 1st Qu.:0.3652 1st Qu.:0.7200 1st Qu.:0.7841 1st Qu.:0.5250
## Median :0.5143 Median :0.8724 Median :0.9502 Median :1.0255
## Mean :0.5399 Mean :0.9020 Mean :0.9759 Mean :1.3357
## 3rd Qu.:0.6996 3rd Qu.:1.1072 3rd Qu.:1.1889 3rd Qu.:1.7271
## Max. :1.4428 Max. :1.4849 Max. :1.6390 Max. :6.6599
## NA's :7 NA's :7 NA's :7 NA's :7
## shoot_foil shoot_foil_dw xs_shoot_mass_fw xs_shoot_foil
## Min. :0.4336 Min. :0.4864 Min. : 0.0243 Min. :0.5372
## 1st Qu.:1.0716 1st Qu.:1.2224 1st Qu.: 2.9386 1st Qu.:1.1943
## Median :1.3160 Median :1.5332 Median : 6.2291 Median :1.5204
## Mean :1.3830 Mean :1.5803 Mean :10.4281 Mean :1.8368
## 3rd Qu.:1.7837 3rd Qu.:2.0987 3rd Qu.:13.7739 3rd Qu.:2.0028
## Max. :2.8118 Max. :2.9439 Max. :66.8455 Max. :5.3852
## NA's :7 NA's :7 NA's :416 NA's :416
## xs_shoot_foil_dw xs_pad_mass_g xs_epiphyte_pad_mass_g iso_count
## Min. : 0.6509 Min. :0.7107 Min. :0.7444 Min. :1.000
## 1st Qu.: 1.8682 1st Qu.:0.7107 1st Qu.:0.7444 1st Qu.:1.000
## Median : 2.4118 Median :0.7107 Median :0.7444 Median :1.000
## Mean : 3.1024 Mean :0.7107 Mean :0.7444 Mean :1.487
## 3rd Qu.: 3.2686 3rd Qu.:0.7107 3rd Qu.:0.7444 3rd Qu.:2.000
## Max. :12.5068 Max. :0.7107 Max. :0.7444 Max. :5.000
## NA's :416 NA's :519 NA's :519 NA's :481
## iso_fw iso_foil iso_foil_dw gamm_amph_count
## Min. :0.0002 Min. :0.3042 Min. :0.3756 Min. : 1.000
## 1st Qu.:0.0050 1st Qu.:0.4584 1st Qu.:0.4948 1st Qu.: 2.000
## Median :0.1364 Median :0.5225 Median :0.5599 Median : 3.000
## Mean :0.1862 Mean :0.5613 Mean :0.6097 Mean : 5.678
## 3rd Qu.:0.2212 3rd Qu.:0.6674 3rd Qu.:0.7326 3rd Qu.: 8.000
## Max. :1.2169 Max. :0.9067 Max. :0.9509 Max. :33.000
## NA's :481 NA's :481 NA's :481 NA's :461
## gamm_amph_fw gamm_amph_foil gamm_amph_foil_dw caprel_count
## Min. :0.0004 Min. :0.2773 Min. :0.2779 Min. : 1.000
## 1st Qu.:0.0046 1st Qu.:0.4560 1st Qu.:0.4568 1st Qu.: 1.000
## Median :0.0075 Median :0.5098 Median :0.5100 Median : 2.000
## Mean :0.0147 Mean :0.5325 Mean :0.5336 Mean : 4.979
## 3rd Qu.:0.0140 3rd Qu.:0.5708 3rd Qu.:0.5727 3rd Qu.: 5.000
## Max. :0.1460 Max. :1.0428 Max. :1.0438 Max. :28.000
## NA's :462 NA's :462 NA's :462 NA's :472
## caprel_fw caprel_foil caprel_foil_dw limpet_count
## Min. :0.0003 Min. :0.0001 Min. :0.0002 Min. : 1.000
## 1st Qu.:0.0026 1st Qu.:0.4412 1st Qu.:0.4434 1st Qu.: 2.000
## Median :0.0074 Median :0.5155 Median :0.5183 Median : 3.000
## Mean :0.0424 Mean :0.5176 Mean :0.5195 Mean : 4.181
## 3rd Qu.:0.0234 3rd Qu.:0.6414 3rd Qu.:0.6454 3rd Qu.: 5.250
## Max. :0.6490 Max. :1.0819 Max. :1.0860 Max. :15.000
## NA's :476 NA's :473 NA's :473 NA's :448
## limpet_fw limpet_foil limpet_foil_dw gastropod_count
## Min. :0.0037 Min. :0.2127 Min. :0.2152 Min. : 1.000
## 1st Qu.:0.0216 1st Qu.:0.4171 1st Qu.:0.4538 1st Qu.: 1.250
## Median :0.0464 Median :0.5199 Median :0.5500 Median : 2.000
## Mean :0.1183 Mean :0.5230 Mean :0.5587 Mean : 3.405
## 3rd Qu.:0.1445 3rd Qu.:0.6012 3rd Qu.:0.6429 3rd Qu.: 4.000
## Max. :0.8388 Max. :1.0519 Max. :1.0611 Max. :16.000
## NA's :448 NA's :448 NA's :448 NA's :478
## gastropod_fw gastropod_foil gastropod_foil_dw crab_count
## Length:520 Min. :0.0001 Min. :0.0002 Min. : NA
## Class :character 1st Qu.:0.3992 1st Qu.:0.4186 1st Qu.: NA
## Mode :character Median :0.5174 Median :0.5216 Median : NA
## Mean :0.5256 Mean :0.5394 Mean :NaN
## 3rd Qu.:0.6185 3rd Qu.:0.6308 3rd Qu.: NA
## Max. :1.0419 Max. :1.0514 Max. : NA
## NA's :478 NA's :478 NA's :520
## crab_fw crab_foil crab_foil_dw polychaete_count other_animal_notes
## Min. : NA Min. : NA Min. : NA Min. : 1.000 Length:520
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: 3.000 Class :character
## Median : NA Median : NA Median : NA Median : 4.000 Mode :character
## Mean :NaN Mean :NaN Mean :NaN Mean : 5.267
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 6.000
## Max. : NA Max. : NA Max. : NA Max. :35.000
## NA's :520 NA's :520 NA's :520 NA's :419
## notes shoot_dw max_length mean_length
## Length:520 Min. :0.0042 Min. : -Inf Min. : 2.30
## Class :character 1st Qu.:0.0938 1st Qu.: 29.27 1st Qu.: 19.00
## Mode :character Median :0.1576 Median : 43.40 Median : 28.40
## Mean :0.1973 Mean : -Inf Mean : 33.15
## 3rd Qu.:0.2575 3rd Qu.: 57.23 3rd Qu.: 42.40
## Max. :0.8045 Max. :111.70 Max. :105.20
## NA's :7 NA's :7eel_bJuly2 <- na.omit(data.frame(eel_beforeJuly$shoot_dw,
eel_beforeJuly$max_length,
eel_beforeJuly$mean_length,
eel_beforeJuly$shoot_mass_fw))
names(eel_bJuly2) <- c("shoot_dw", "max_length", "mean_length", "shoot_mass_fw")Look at data
plot(eel_sub2$max_length, eel_sub2$shoot_dw)
# Looks like the variance in shoot dw might be increasing with the the length (mm) of the longest blade.
plot(eel_sub2$mean_length, eel_sub2$shoot_dw)
plot(eel_sub2$max_length, eel_sub2$shoot_mass_fw)
# not as tight as a correlation
cor(eel_sub2$max_length, eel_sub2$shoot_dw, use = "complete.obs")## [1] 0.8317317cor(eel_sub2$mean_length, eel_sub2$shoot_dw, use = "complete.obs")## [1] 0.5924666cor(eel_sub2$mean_length, eel_sub2$shoot_mass_fw, use = "complete.obs")## [1] 0.6335515# stronger positive linear relationship with max length than average length (makes sense)
par(mfrow=c(1,2))
boxplot(eel_sub2$max_length)
boxplot(eel_sub2$shoot_dw)
# Looks like both varibles may have some outliers, in particular the shoot dry weight.
par(mfrow=c(1,2))
plot(density(eel_sub2$max_length), ylab = "Frequency")
plot(density(eel_sub2$shoot_dw, na.rm = T), ylab = "Frequency")
hist(eel_sub2$max_length)
# max length looks pretty normal with some slight right? skew
hist(eel_sub2$shoot_dw)
range(eel_sub2$shoot_dw)## [1] 0.0042 1.3277# heavily right skewed does log make it look better?
hist(log(eel_sub2$shoot_dw))
# skews it the other way...
# lets write out the appropriately cleaned 2017 eelgrass data so we can quickly use it to convert eelgrass length to biomass in the future
#write.csv(eel_sub2, file = "Data/eelgrass_conversions_for_lm_2017")
Linear models
All Data
12/7/20 removed july filter## data only before july
dat <- na.omit(data.frame(eel_sub2$shoot_dw, eel_sub2$max_length))
names(dat) <- c("dw", "max_length")
plot(dat$dw, dat$max_length)
# Lets fit an untransformed linear model
fit.lm <- lm(dw ~ max_length, data = dat)
par(mfrow=c(2,2))
plot(fit.lm, which = 1:4)
# looks like there might be some funky stuff happening with the residuals -- maybe an increase in variance along the fitted values. And the QQ plot looks real gross with some concavity (i.e. skewness) and some possible outliers. The cooks plot makes it look like there case numbers (171, 230, 498) could be influencial outliers. Some of this is consisten with the histogram of the data (see above)
summary(fit.lm)##
## Call:
## lm(formula = dw ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29147 -0.05080 -0.00870 0.02743 0.87255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0969956 0.0089384 -10.85 <2e-16 ***
## max_length 0.0065732 0.0001522 43.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1057 on 831 degrees of freedom
## Multiple R-squared: 0.6918, Adjusted R-squared: 0.6914
## F-statistic: 1865 on 1 and 831 DF, p-value: < 2.2e-16AIC(fit.lm); BIC(fit.lm)## [1] -1375.169## [1] -1360.994# looks like max length is significantly different than 0
# Has an adjusted r2 of 69%... pretty good for ecology. Model is significant, coefficients for the model are not equal to zero (p value =~0)
library(e1071)
skewness(dat$dw)## [1] 1.577212skewness(dat$max_length)## [1] 0.6910809# the response variable is skewed.... highly skewed
e <- residuals(fit.lm)
shapiro.test(e)##
## Shapiro-Wilk normality test
##
## data: e
## W = 0.82894, p-value < 2.2e-16n <- length(dat$dw)
ggplot(dat, aes(x = max_length, y = dw)) + geom_point() + theme_classic() +
geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
# Does a boxcox indicate a transformation is necessary?
library(MASS)
boxcox(fit.lm)
boxcox(fit.lm, lambda=seq(from=0, to=0.6, by=.01))
# Umm of like square root?
# Could also try a log
hist(dat$dw^0.5)
y.2 <- dat$y^(0.5)
# looks way better with a fourth root transformation, normal distribution
fit2 <- lm(dw^(0.5) ~ max_length, data = dat)
plot(fit2)
# looks a lot better, homoscedascity is good, qq plot tails are a little weird, still some outliers
summary(fit2)##
## Call:
## lm(formula = dw^(0.5) ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25974 -0.05257 -0.00637 0.03766 0.48356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1283679 0.0073131 17.55 <2e-16 ***
## max_length 0.0064325 0.0001245 51.66 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08651 on 831 degrees of freedom
## Multiple R-squared: 0.7625, Adjusted R-squared: 0.7622
## F-statistic: 2668 on 1 and 831 DF, p-value: < 2.2e-16# r squared is better, model is still significant
plot(dat$max_length, dat$dw^(0.5))
# Plot the square root transformed data
lm_eqn <- function(dat){
m <- fit2;
eq <- substitute(italic(sqrt(y)) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
ggplot(dat, aes(x = max_length, y = dw^(0.5))) +
geom_point() +
geom_smooth(method = "lm", se = TRUE, level = 0.95) +
geom_text(x = 25, y = 0.8, label = lm_eqn(dat), parse = TRUE,
check_overlap = TRUE) +
theme_classic()## `geom_smooth()` using formula = 'y ~ x'
# Compare to a logtransformed model (easier to understand)
fit3 <- lm(log(dw) ~ max_length, data = dat)
plot(fit3)
summary(fit3)##
## Call:
## lm(formula = log(dw) ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.49183 -0.22835 0.04015 0.25637 1.41016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.1842390 0.0365173 -87.20 <2e-16 ***
## max_length 0.0286471 0.0006218 46.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.432 on 831 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.7183
## F-statistic: 2122 on 1 and 831 DF, p-value: < 2.2e-16hist(log(dat$dw))
plot(dat$max_length, log(dat$dw))
# log-log transformation
lm2 <- lm(log(shoot_dw) ~ log(max_length), data = eel_sub2)
summary(lm2)##
## Call:
## lm(formula = log(shoot_dw) ~ log(max_length), data = eel_sub2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.13653 -0.22248 0.00754 0.18710 1.30992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.67725 0.09434 -81.38 <2e-16 ***
## log(max_length) 1.55564 0.02416 64.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3328 on 831 degrees of freedom
## Multiple R-squared: 0.833, Adjusted R-squared: 0.8328
## F-statistic: 4144 on 1 and 831 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(lm2, which = 1:4) # some issues but voerall looks really nice
# plot the log-log transformed data with linear model
lm_eqn <- function(dat){
m <- lm2;
eq <- substitute(italic(log(y)) == a + b %.% italic(log(x))*","~~italic(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
ggplot(dat, aes(x = log(max_length), y = log(dw))) + geom_point() + geom_smooth(method = "lm", se = TRUE, level = 0.95) + geom_text(x = 2.5, y = -0.5, label = lm_eqn(dat), parse = TRUE, check_overlap = TRUE) +
theme_classic()## `geom_smooth()` using formula = 'y ~ x'
visreg(lm2, gg = T)
visreg(lm2, gg = T, scale = "response")
ggplot(data=eel_sub2, aes(lm2$residuals)) +
geom_histogram(color = "black", fill = "purple4") +
theme(panel.background = element_rect(fill = "white"),
axis.line.x=element_line(),
axis.line.y=element_line()) +
ggtitle("Histogram for Model Residuals") # damns those residuals are NICE. Pretty symmetrical around 0 -- the model fits teh data pretty well ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = dat, aes(x = log(max_length), y = log(dw))) +
geom_point() +
stat_smooth(method = "lm", col = "dodgerblue3") +
theme(panel.background = element_rect(fill = "white"),
axis.line.x=element_line(),
axis.line.y=element_line()) +
ggtitle("Linear Model Fitted to Data")## `geom_smooth()` using formula = 'y ~ x'
## Before July Which fit of linear model is better? We know that the log-log relationship is better so lets try that and then compare the two model types
dat2 <- na.omit(data.frame(eel_bJuly2$shoot_dw, eel_bJuly2$max_length))
names(dat2) <- c("dw", "max_length")
lm4 <- lm(log(dw) ~ log(max_length), data = dat2)
summary(lm4)##
## Call:
## lm(formula = log(dw) ~ log(max_length), data = dat2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0858 -0.2056 0.0128 0.1736 1.2739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.78423 0.11222 -69.37 <2e-16 ***
## log(max_length) 1.57549 0.02987 52.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3237 on 511 degrees of freedom
## Multiple R-squared: 0.8448, Adjusted R-squared: 0.8445
## F-statistic: 2781 on 1 and 511 DF, p-value: < 2.2e-16Use selected linear model to predict 2019 biomass data
Import 2019 data
The data for 2019: is set up in a 3 sheet excel spreadsheets. quadrat_ID in hab_qrt is unique for every quadrat that was done in summer 2019. Use the quadrat_ID to connect to the other sheets (hab_lng and hab_wgt). Hab_lng has all the individual lengths for all measured plants (in mm) (for eelgrass its 15 blades from each quadrat/site. For kelp its up to 3 of each species collected from the site). Hab_wgt has the biomass weights for individual species biomass by bag
For this purpose we are interested in only the length data but need to use the quadrat sheet to make sure we only are looking at the eelgrass sites newest version of data is : RAW_10-22-19
hab_qrt <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A33a4f3b6-ad30-494d-a0bf-861fe86d729e"), stringsAsFactors = FALSE, header = TRUE)
hab_lng <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Af7c43f66-6800-49b0-9b3d-43e8e39abcd0"), stringsAsFactors=FALSE, header=TRUE)
#hab_wgt <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A3b4ab5b8-c023-486f-8a89-7464b9bccf7a"))
loc <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Ac9c99ce9-fbdd-4879-a2c9-c90448cdba7b"), stringsAsFactors = FALSE, header = TRUE)Data cleaning
# adjust the site names in qrt data so that we can combine with
hab_qrt$site <- as.factor(hab_qrt$site)
levels(hab_qrt$site)[levels(hab_qrt$site)=="Goats mouth inlet"]<-"Goat Mouth Inlet"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Naukati Bay"]<-"Naukati"
levels(hab_qrt$site)[levels(hab_qrt$site)=="South Wadleigh Island"]<-"South Wadleigh"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Guktu Bay"]<-"Guktu"
levels(hab_qrt$site)[levels(hab_qrt$site)=="North Fish Egg Island"]<-"North Fish Egg"
levels(hab_qrt$site)[levels(hab_qrt$site)=="North Fish Egg - kelp"]<-"North Fish Egg-Kelp"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Natzuhini Bay"]<-"Natzuhini"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Kaguk Cove"]<-"Kaguk "
levels(hab_qrt$site)[levels(hab_qrt$site)=="Farallon Bay"]<-"Farallon"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Chusini Cove"]<-"Chusini-Kladein Flat"
levels(hab_qrt$site)[levels(hab_qrt$site)=="South Fish Egg Island"]<-"South Fish Egg"
levels(hab_qrt$site)[levels(hab_qrt$site)=="Baker Island - kelp"]<-"Baker Island - kelp "
hab_qrt$site <- as.character(hab_qrt$site)
hab_qrt <- left_join(hab_qrt, loc, by = c("site" = "site_2019", "habitat" = "habitat")) %>%
dplyr::select(-c(siteID_NOAA, site_2017, site_2018, place_name, study, latitude, longitude,
freshwater, sediment_description, general_description)) %>%
filter(habitat == "eelgrass")We want only the eelgrass sites and need to convert the length measurements (in mm) to cm
glimpse(hab_qrt)## Rows: 100
## Columns: 13
## $ X <int> 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2…
## $ quadrat_ID <int> 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2…
## $ site <chr> "Shakan - eel", "Shakan - eel", "Shakan - eel", "Shak…
## $ date <chr> "2019-06-07", "2019-06-07", "2019-06-07", "2019-06-07…
## $ YYYYMMDD <int> 20190607, 20190607, 20190607, 20190607, 20190607, 201…
## $ habitat <chr> "eelgrass", "eelgrass", "eelgrass", "eelgrass", "eelg…
## $ quadrat <int> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3,…
## $ total_biomass <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ density <int> 29, 18, 39, 28, 48, 138, 246, 309, 255, 156, 71, 95, …
## $ flowering_shoots <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ notes <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ bay_code <chr> "CALD", "CALD", "CALD", "CALD", "CALD", "BAKE", "BAKE…
## $ bay_sample <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"…# Want to only extract the quadrat numbers that were used to sample eelgrass sites
eel_qrts <- na.omit(ifelse(hab_qrt$habitat == "eelgrass", paste(hab_qrt$quadrat_ID), NA))
# subset the length data by quadrat at an eelgrass site
lng_sub <- subset(hab_lng, quadrat_ID %in% eel_qrts)
levels(as.factor(lng_sub$species)) # only Z. marina, awesome! ## [1] "Zostera marina"lng_sub <- lng_sub %>%
mutate(length_cm = length/10)Predict based on linear models
all data (lm2)
summary(lm2)##
## Call:
## lm(formula = log(shoot_dw) ~ log(max_length), data = eel_sub2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.13653 -0.22248 0.00754 0.18710 1.30992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.67725 0.09434 -81.38 <2e-16 ***
## log(max_length) 1.55564 0.02416 64.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3328 on 831 degrees of freedom
## Multiple R-squared: 0.833, Adjusted R-squared: 0.8328
## F-statistic: 4144 on 1 and 831 DF, p-value: < 2.2e-16# formula :
# log (y) = beta0 + beta1 * log (x)
# log (dw) = -7.67 + 1.55 * log (max_length)
# create a new dataframe with just the leaf lengths from 2019
newx <- data.frame(max_length = as.numeric(lng_sub$length_cm))
# use the log-log linear model (equation above) to calculate the dry mass of the shoot with 95% CI
# make sure to exp() the values because its a log-log relationship
pr.lm <- exp(predict(lm2, newdata = newx, interval = "confidence", level = 0.95))
# graph these new predicted values
# create data.frame first
newdata <- cbind(lng_sub, pr.lm)
ggplot(newdata, aes(x = length_cm, y = fit)) + geom_point() + theme_classic() + geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
Estimate total biomass (lm2)
str(newdata)## 'data.frame': 1503 obs. of 9 variables:
## $ X : int 27 28 29 30 31 32 33 34 35 36 ...
## $ plant_ID : int 27 28 29 30 31 32 33 34 35 36 ...
## $ quadrat_ID: int 6 6 6 6 6 6 6 6 6 6 ...
## $ species : chr "Zostera marina" "Zostera marina" "Zostera marina" "Zostera marina" ...
## $ length : int 579 835 375 334 691 323 581 418 852 544 ...
## $ length_cm : num 57.9 83.5 37.5 33.4 69.1 32.3 58.1 41.8 85.2 54.4 ...
## $ fit : num 0.256 0.452 0.13 0.109 0.337 ...
## $ lwr : num 0.25 0.437 0.127 0.106 0.327 ...
## $ upr : num 0.262 0.468 0.134 0.112 0.346 ...str(hab_qrt)## 'data.frame': 100 obs. of 13 variables:
## $ X : int 6 7 8 9 10 11 12 13 14 15 ...
## $ quadrat_ID : int 6 7 8 9 10 11 12 13 14 15 ...
## $ site : chr "Shakan - eel" "Shakan - eel" "Shakan - eel" "Shakan - eel" ...
## $ date : chr "2019-06-07" "2019-06-07" "2019-06-07" "2019-06-07" ...
## $ YYYYMMDD : int 20190607 20190607 20190607 20190607 20190607 20190618 20190618 20190618 20190618 20190618 ...
## $ habitat : chr "eelgrass" "eelgrass" "eelgrass" "eelgrass" ...
## $ quadrat : int 1 2 3 4 5 1 2 3 4 5 ...
## $ total_biomass : num NA NA NA NA NA NA NA NA NA NA ...
## $ density : int 29 18 39 28 48 138 246 309 255 156 ...
## $ flowering_shoots: int 0 0 0 0 0 0 0 0 0 0 ...
## $ notes : chr NA NA NA NA ...
## $ bay_code : chr "CALD" "CALD" "CALD" "CALD" ...
## $ bay_sample : chr "A" "A" "A" "A" ...hab_qrt$density <- as.numeric(hab_qrt$density)
hab_qrt$flowering_shoots <- as.numeric(hab_qrt$flowering_shoots)
# Want to only extract the quadrat numbers that were used to sample eelgrass sites
dsty <- hab_qrt %>%
filter(habitat == 'eelgrass') %>%
mutate(density_m2 = (density)*4, density_0.25msq = density) %>%
mutate(flowering_m2 = (flowering_shoots)*4, flowering_0.25msq = flowering_shoots)
dsty <- dplyr::select(dsty, quadrat_ID, site, date, YYYYMMDD,
habitat, quadrat, total_biomass, density_m2, flowering_m2,
bay_code, bay_sample, notes, density_0.25msq, flowering_0.25msq)
# calculate average biomass for 15 shoots for each quadrat
require(dplyr)
df <- newdata %>%
group_by(quadrat_ID) %>%
mutate(avg_biomass = mean(fit))
df1 <- df %>%
dplyr::select(quadrat_ID, avg_biomass) %>%
distinct()
# need to add back in site information
df2 <- left_join(df1, dsty, by = "quadrat_ID")
df3 <- left_join(df2, hab_qrt[,1:2], by = "quadrat_ID")
#write.csv(df3, "../APECS Master repository/APECS Master repo/ALL_DATA/seagrass_biomass_conversions_baycodes.csv") Before July
all data (lm4)
summary(lm4)##
## Call:
## lm(formula = log(dw) ~ log(max_length), data = dat2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0858 -0.2056 0.0128 0.1736 1.2739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.78423 0.11222 -69.37 <2e-16 ***
## log(max_length) 1.57549 0.02987 52.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3237 on 511 degrees of freedom
## Multiple R-squared: 0.8448, Adjusted R-squared: 0.8445
## F-statistic: 2781 on 1 and 511 DF, p-value: < 2.2e-16# formula :
# log (y) = beta0 + beta1 * log (x)
# log (dw) = -7.78 + 1.57 * log (max_length)
# create a new dataframe with just the leaf lengths from 2019
newx_lm4 <- data.frame(max_length = as.numeric(lng_sub$length_cm))
# use the log-log linear model (equation above) to calculate the dry mass of the shoot with 95% CI
# make sure to exp() the values because its a log-log relationship
pr.lm4 <- exp(predict(lm4, newdata = newx_lm4, interval = "confidence", level = 0.95))
# graph these new predicted values
# create data.frame first
newdata_lm4 <- cbind(lng_sub, pr.lm4)
ggplot(newdata_lm4, aes(x = length_cm, y = fit)) + geom_point() + theme_classic() + geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
Estimate total biomass (lm2)
str(newdata_lm4)## 'data.frame': 1503 obs. of 9 variables:
## $ X : int 27 28 29 30 31 32 33 34 35 36 ...
## $ plant_ID : int 27 28 29 30 31 32 33 34 35 36 ...
## $ quadrat_ID: int 6 6 6 6 6 6 6 6 6 6 ...
## $ species : chr "Zostera marina" "Zostera marina" "Zostera marina" "Zostera marina" ...
## $ length : int 579 835 375 334 691 323 581 418 852 544 ...
## $ length_cm : num 57.9 83.5 37.5 33.4 69.1 32.3 58.1 41.8 85.2 54.4 ...
## $ fit : num 0.249 0.444 0.126 0.105 0.329 ...
## $ lwr : num 0.241 0.422 0.122 0.102 0.316 ...
## $ upr : num 0.258 0.466 0.129 0.108 0.343 ...str(hab_qrt)## 'data.frame': 100 obs. of 13 variables:
## $ X : int 6 7 8 9 10 11 12 13 14 15 ...
## $ quadrat_ID : int 6 7 8 9 10 11 12 13 14 15 ...
## $ site : chr "Shakan - eel" "Shakan - eel" "Shakan - eel" "Shakan - eel" ...
## $ date : chr "2019-06-07" "2019-06-07" "2019-06-07" "2019-06-07" ...
## $ YYYYMMDD : int 20190607 20190607 20190607 20190607 20190607 20190618 20190618 20190618 20190618 20190618 ...
## $ habitat : chr "eelgrass" "eelgrass" "eelgrass" "eelgrass" ...
## $ quadrat : int 1 2 3 4 5 1 2 3 4 5 ...
## $ total_biomass : num NA NA NA NA NA NA NA NA NA NA ...
## $ density : num 29 18 39 28 48 138 246 309 255 156 ...
## $ flowering_shoots: num 0 0 0 0 0 0 0 0 0 0 ...
## $ notes : chr NA NA NA NA ...
## $ bay_code : chr "CALD" "CALD" "CALD" "CALD" ...
## $ bay_sample : chr "A" "A" "A" "A" ...hab_qrt$density <- as.numeric(hab_qrt$density)
hab_qrt$flowering_shoots <- as.numeric(hab_qrt$flowering_shoots)
# Want to only extract the quadrat numbers that were used to sample eelgrass sites
dsty <- hab_qrt %>%
filter(habitat == 'eelgrass') %>%
mutate(density_m2 = (density)*4) %>%
mutate(flowering_m2 = (flowering_shoots)*4) %>%
dplyr::select(c(quadrat_ID, density, flowering_shoots, bay_code,
bay_sample, density_m2, flowering_m2))
#dsty <- dsty[,c(1,8,9,11,12)]
# calculate average biomass for 15 shoots for each quadrat
require(dplyr)
df_lm4 <- newdata_lm4 %>%
group_by(quadrat_ID) %>%
mutate(avg_biomass = mean(fit))
df1_lm4 <- df_lm4 %>%
dplyr::select(quadrat_ID, avg_biomass) %>%
distinct()
# need to add back in site information
df2_lm4 <- left_join(df1_lm4, dsty, by = "quadrat_ID")
df3_lm4 <- left_join(df2_lm4, hab_qrt[,c("quadrat_ID", "site")], by = "quadrat_ID")
#write.csv(df3_lm4, "../ALL_DATA/seagrass_biomass_conversions.csv") Look at variation in biomass between years by site
2017
eel17 <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A5e946e41-4f5f-4499-9969-766f01113971"),
stringsAsFactors = FALSE, header = TRUE) # density data
eel_biom17 <- eel_sub %>%
group_by(site, quadrat) %>%
mutate(avg_biom_per_quad = mean(shoot_dw)) %>%
dplyr::select(site:plant, avg_biom_per_quad, mean_length,
max_length, shoot_dw,rhi_length:notes)
eel_biom_dens17 <- eel_biom17 %>%
dplyr::select(site:avg_biom_per_quad, -plant) %>%
distinct() %>%
left_join(eel17, by = c("site", "quadrat")) %>%
mutate(total_shoots_0.25msq = eelgrass_shoots_0.25msq + flowering_shoots_0.25msq) %>%
mutate(shoots_1msq = total_shoots_0.25msq * 4) %>%
dplyr::select(site:YYYYMMDD.y, depth_m,eelgrass_shoots_0.25msq:shoots_1msq, -notes) %>%
mutate(biom_per_quad_0.25msq = avg_biom_per_quad * total_shoots_0.25msq) %>%
mutate(biom_msq = biom_per_quad_0.25msq * 4)
biomass_var_2017 <- eel_biom_dens17 %>%
ggplot() +
geom_boxplot(aes(y = biom_msq, x = site)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
biomass_var_2017## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).
# write.csv(eel_biom_dens17, "eelgrass_biom_density_2017.csv")biomass_var_2019 <- df3_lm4 %>%
mutate(biom_msq = avg_biomass * density_m2) %>%
ggplot() +
geom_boxplot(aes(y = biom_msq, x = site)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
biomass_var_2019_alldat <- df3 %>%
mutate(biom_msq = avg_biomass * density_m2) %>%
ggplot() +
geom_boxplot(aes(y = biom_msq, x = site)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
plot_grid(biomass_var_2019, biomass_var_2019_alldat,
nrow=1, ncol=2)
compare 2017 and 2019 biomass
plot_grid(biomass_var_2017, biomass_var_2019_alldat,
nrow=1, ncol =2 )## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).